Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations5000
Missing cells7960
Missing cells (%)13.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory468.9 KiB
Average record size in memory96.0 B

Variable types

Numeric9
Text3

Alerts

Average_SAT_Score is highly overall correlated with Completion_Rate and 4 other fieldsHigh correlation
Completion_Rate is highly overall correlated with Average_SAT_ScoreHigh correlation
In-State_Tuition is highly overall correlated with Average_SAT_Score and 3 other fieldsHigh correlation
Median_Earnings_10_Years_After is highly overall correlated with Average_SAT_Score and 3 other fieldsHigh correlation
Out-of-State_Tuition is highly overall correlated with Average_SAT_Score and 3 other fieldsHigh correlation
Pell_Grant_Rate is highly overall correlated with Average_SAT_ScoreHigh correlation
Student_Size is highly overall correlated with In-State_Tuition and 2 other fieldsHigh correlation
Admission_Rate has 3200 (64.0%) missing valuesMissing
Average_SAT_Score has 3931 (78.6%) missing valuesMissing
Completion_Rate has 577 (11.5%) missing valuesMissing
Pell_Grant_Rate has 252 (5.0%) missing valuesMissing
id has unique valuesUnique
In-State_Tuition has 1824 (36.5%) zerosZeros
Out-of-State_Tuition has 1824 (36.5%) zerosZeros
Student_Size has 245 (4.9%) zerosZeros
Median_Earnings_10_Years_After has 445 (8.9%) zerosZeros

Reproduction

Analysis started2024-09-24 19:54:07.660261
Analysis finished2024-09-24 19:54:15.191259
Duration7.53 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Admission_Rate
Real number (ℝ)

MISSING 

Distinct1458
Distinct (%)81.0%
Missing3200
Missing (%)64.0%
Infinite0
Infinite (%)0.0%
Mean0.72397144
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-09-24T15:54:15.268201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.228405
Q10.61415
median0.77615
Q30.891225
95-th percentile0.994705
Maximum1
Range1
Interquartile range (IQR)0.277075

Descriptive statistics

Standard deviation0.22412041
Coefficient of variation (CV)0.30957079
Kurtosis0.76519584
Mean0.72397144
Median Absolute Deviation (MAD)0.1337
Skewness-1.0974255
Sum1303.1486
Variance0.050229959
MonotonicityNot monotonic
2024-09-24T15:54:15.391938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 79
 
1.6%
0.5 6
 
0.1%
0.75 6
 
0.1%
0.4 5
 
0.1%
0.6667 5
 
0.1%
0.8571 5
 
0.1%
0.9429 5
 
0.1%
0.8 5
 
0.1%
0.551 4
 
0.1%
0.3333 4
 
0.1%
Other values (1448) 1676
33.5%
(Missing) 3200
64.0%
ValueCountFrequency (%)
0 1
< 0.1%
0.0106 1
< 0.1%
0.0269 1
< 0.1%
0.0324 1
< 0.1%
0.0368 1
< 0.1%
0.0395 1
< 0.1%
0.0396 1
< 0.1%
0.0457 1
< 0.1%
0.0506 1
< 0.1%
0.0543 1
< 0.1%
ValueCountFrequency (%)
1 79
1.6%
0.9994 1
 
< 0.1%
0.9992 1
 
< 0.1%
0.9985 1
 
< 0.1%
0.9977 1
 
< 0.1%
0.9966 1
 
< 0.1%
0.9956 1
 
< 0.1%
0.9955 1
 
< 0.1%
0.9951 1
 
< 0.1%
0.995 1
 
< 0.1%

In-State_Tuition
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2601
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11011.365
Minimum0
Maximum68365
Zeros1824
Zeros (%)36.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-09-24T15:54:15.605932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4740
Q314878.5
95-th percentile46001.5
Maximum68365
Range68365
Interquartile range (IQR)14878.5

Descriptive statistics

Standard deviation15178.646
Coefficient of variation (CV)1.3784527
Kurtosis2.1129572
Mean11011.365
Median Absolute Deviation (MAD)4740
Skewness1.6837877
Sum55056825
Variance2.303913 × 108
MonotonicityNot monotonic
2024-09-24T15:54:15.740511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1824
36.5%
13515 17
 
0.3%
4560 14
 
0.3%
12750 12
 
0.2%
4920 10
 
0.2%
2070 10
 
0.2%
16757 9
 
0.2%
1238 9
 
0.2%
5580 8
 
0.2%
4380 8
 
0.2%
Other values (2591) 3079
61.6%
ValueCountFrequency (%)
0 1824
36.5%
480 1
 
< 0.1%
744 1
 
< 0.1%
932 1
 
< 0.1%
1008 1
 
< 0.1%
1104 1
 
< 0.1%
1108 1
 
< 0.1%
1114 1
 
< 0.1%
1124 2
 
< 0.1%
1126 1
 
< 0.1%
ValueCountFrequency (%)
68365 1
< 0.1%
66490 1
< 0.1%
66139 1
< 0.1%
65844 1
< 0.1%
65222 1
< 0.1%
65146 1
< 0.1%
65028 1
< 0.1%
64951 1
< 0.1%
64800 1
< 0.1%
64760 1
< 0.1%

Out-of-State_Tuition
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2663
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13344.035
Minimum0
Maximum68365
Zeros1824
Zeros (%)36.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-09-24T15:54:15.865083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8751
Q320000.25
95-th percentile46184.5
Maximum68365
Range68365
Interquartile range (IQR)20000.25

Descriptive statistics

Standard deviation15470.764
Coefficient of variation (CV)1.1593768
Kurtosis1.0435399
Mean13344.035
Median Absolute Deviation (MAD)8751
Skewness1.2899022
Sum66720176
Variance2.3934455 × 108
MonotonicityNot monotonic
2024-09-24T15:54:15.982957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1824
36.5%
13515 17
 
0.3%
15480 13
 
0.3%
12750 12
 
0.2%
7854 10
 
0.2%
9870 9
 
0.2%
16757 9
 
0.2%
8670 8
 
0.2%
14430 8
 
0.2%
20794 7
 
0.1%
Other values (2653) 3083
61.7%
ValueCountFrequency (%)
0 1824
36.5%
480 1
 
< 0.1%
932 1
 
< 0.1%
1008 1
 
< 0.1%
1410 1
 
< 0.1%
1600 2
 
< 0.1%
2210 1
 
< 0.1%
2250 1
 
< 0.1%
2260 1
 
< 0.1%
2280 1
 
< 0.1%
ValueCountFrequency (%)
68365 1
< 0.1%
66490 1
< 0.1%
66139 1
< 0.1%
65844 1
< 0.1%
65222 1
< 0.1%
65146 1
< 0.1%
65028 1
< 0.1%
64951 1
< 0.1%
64800 1
< 0.1%
64760 1
< 0.1%

Student_Size
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2604
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2662.044
Minimum0
Maximum138138
Zeros245
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-09-24T15:54:16.079706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q1126.75
median665
Q32403
95-th percentile11989.4
Maximum138138
Range138138
Interquartile range (IQR)2276.25

Descriptive statistics

Standard deviation6043.4248
Coefficient of variation (CV)2.2702197
Kurtosis90.157958
Mean2662.044
Median Absolute Deviation (MAD)616
Skewness6.8467317
Sum13310220
Variance36522983
MonotonicityNot monotonic
2024-09-24T15:54:16.177971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 245
 
4.9%
43 16
 
0.3%
73 15
 
0.3%
115 14
 
0.3%
39 14
 
0.3%
68 14
 
0.3%
37 14
 
0.3%
45 13
 
0.3%
40 13
 
0.3%
142 13
 
0.3%
Other values (2594) 4629
92.6%
ValueCountFrequency (%)
0 245
4.9%
1 2
 
< 0.1%
2 5
 
0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 5
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 7
 
0.1%
9 6
 
0.1%
ValueCountFrequency (%)
138138 1
< 0.1%
112807 1
< 0.1%
65147 1
< 0.1%
64778 1
< 0.1%
57874 1
< 0.1%
56792 1
< 0.1%
51796 1
< 0.1%
48408 1
< 0.1%
47486 1
< 0.1%
45140 1
< 0.1%

Average_SAT_Score
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct446
Distinct (%)41.7%
Missing3931
Missing (%)78.6%
Infinite0
Infinite (%)0.0%
Mean1172.811
Minimum850
Maximum1560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-09-24T15:54:16.280991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum850
5-th percentile970.4
Q11079
median1147
Q31250
95-th percentile1472.6
Maximum1560
Range710
Interquartile range (IQR)171

Descriptive statistics

Standard deviation143.59361
Coefficient of variation (CV)0.12243542
Kurtosis0.082307647
Mean1172.811
Median Absolute Deviation (MAD)85
Skewness0.64414703
Sum1253735
Variance20619.125
MonotonicityNot monotonic
2024-09-24T15:54:16.386890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1090 34
 
0.7%
1050 28
 
0.6%
1120 17
 
0.3%
1150 15
 
0.3%
1220 12
 
0.2%
1190 11
 
0.2%
1110 9
 
0.2%
1147 9
 
0.2%
1020 9
 
0.2%
1089 8
 
0.2%
Other values (436) 917
 
18.3%
(Missing) 3931
78.6%
ValueCountFrequency (%)
850 1
< 0.1%
862 1
< 0.1%
870 1
< 0.1%
871 1
< 0.1%
878 1
< 0.1%
887 1
< 0.1%
889 1
< 0.1%
890 1
< 0.1%
895 1
< 0.1%
896 1
< 0.1%
ValueCountFrequency (%)
1560 1
 
< 0.1%
1554 1
 
< 0.1%
1553 3
0.1%
1547 2
 
< 0.1%
1546 5
0.1%
1540 2
 
< 0.1%
1539 2
 
< 0.1%
1533 1
 
< 0.1%
1532 1
 
< 0.1%
1527 2
 
< 0.1%

Median_Earnings_10_Years_After
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3877
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39888.984
Minimum0
Maximum143372
Zeros445
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-09-24T15:54:16.500491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q128692.5
median39238
Q351184
95-th percentile74480.15
Maximum143372
Range143372
Interquartile range (IQR)22491.5

Descriptive statistics

Standard deviation20583.439
Coefficient of variation (CV)0.51601813
Kurtosis1.3598649
Mean39888.984
Median Absolute Deviation (MAD)11411.5
Skewness0.35228276
Sum1.9944492 × 108
Variance4.2367796 × 108
MonotonicityNot monotonic
2024-09-24T15:54:16.617864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 445
 
8.9%
63435 21
 
0.4%
31102 18
 
0.4%
40092 17
 
0.3%
31349 12
 
0.2%
32986 11
 
0.2%
38673 10
 
0.2%
36909 9
 
0.2%
23921 9
 
0.2%
25073 9
 
0.2%
Other values (3867) 4439
88.8%
ValueCountFrequency (%)
0 445
8.9%
8579 1
 
< 0.1%
9656 1
 
< 0.1%
11575 1
 
< 0.1%
11585 1
 
< 0.1%
11998 1
 
< 0.1%
12610 1
 
< 0.1%
12635 1
 
< 0.1%
13256 1
 
< 0.1%
13265 1
 
< 0.1%
ValueCountFrequency (%)
143372 1
< 0.1%
143238 1
< 0.1%
138767 1
< 0.1%
138687 1
< 0.1%
137047 1
< 0.1%
131426 1
< 0.1%
129455 1
< 0.1%
128566 1
< 0.1%
125557 1
< 0.1%
124080 1
< 0.1%

Completion_Rate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3192
Distinct (%)72.2%
Missing577
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean0.53513039
Minimum0.0106
Maximum0.9897
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-09-24T15:54:16.721425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0106
5-th percentile0.19762
Q10.3681
median0.5448
Q30.6956
95-th percentile0.87257
Maximum0.9897
Range0.9791
Interquartile range (IQR)0.3275

Descriptive statistics

Standard deviation0.2093589
Coefficient of variation (CV)0.39122969
Kurtosis-0.84582402
Mean0.53513039
Median Absolute Deviation (MAD)0.1632
Skewness-0.042470622
Sum2366.8817
Variance0.043831147
MonotonicityNot monotonic
2024-09-24T15:54:16.832807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 15
 
0.3%
0.75 14
 
0.3%
0.6 13
 
0.3%
0.6667 12
 
0.2%
0.7 10
 
0.2%
0.8 9
 
0.2%
0.625 9
 
0.2%
0.7143 8
 
0.2%
0.6842 7
 
0.1%
0.7667 7
 
0.1%
Other values (3182) 4319
86.4%
(Missing) 577
 
11.5%
ValueCountFrequency (%)
0.0106 1
< 0.1%
0.0204 1
< 0.1%
0.0345 2
< 0.1%
0.0392 1
< 0.1%
0.05 1
< 0.1%
0.0517 1
< 0.1%
0.054 1
< 0.1%
0.0625 1
< 0.1%
0.0629 1
< 0.1%
0.0675 1
< 0.1%
ValueCountFrequency (%)
0.9897 1
< 0.1%
0.9875 1
< 0.1%
0.9856 1
< 0.1%
0.9815 1
< 0.1%
0.9813 1
< 0.1%
0.981 1
< 0.1%
0.9808 1
< 0.1%
0.98 2
< 0.1%
0.9792 1
< 0.1%
0.9762 1
< 0.1%

Pell_Grant_Rate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3359
Distinct (%)70.7%
Missing252
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean0.41921866
Minimum0
Maximum1
Zeros46
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-09-24T15:54:16.943485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.13277
Q10.265975
median0.3875
Q30.55365
95-th percentile0.803595
Maximum1
Range1
Interquartile range (IQR)0.287675

Descriptive statistics

Standard deviation0.20594326
Coefficient of variation (CV)0.491255
Kurtosis-0.25718855
Mean0.41921866
Median Absolute Deviation (MAD)0.1391
Skewness0.50588814
Sum1990.4502
Variance0.042412627
MonotonicityNot monotonic
2024-09-24T15:54:17.044048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46
 
0.9%
1 22
 
0.4%
0.5 16
 
0.3%
0.6 11
 
0.2%
0.3333 10
 
0.2%
0.75 9
 
0.2%
0.6667 9
 
0.2%
0.8 8
 
0.2%
0.3921 7
 
0.1%
0.2308 6
 
0.1%
Other values (3349) 4604
92.1%
(Missing) 252
 
5.0%
ValueCountFrequency (%)
0 46
0.9%
0.0078 1
 
< 0.1%
0.0124 1
 
< 0.1%
0.0158 1
 
< 0.1%
0.0159 1
 
< 0.1%
0.0173 1
 
< 0.1%
0.0233 1
 
< 0.1%
0.027 1
 
< 0.1%
0.0311 1
 
< 0.1%
0.0325 1
 
< 0.1%
ValueCountFrequency (%)
1 22
0.4%
0.9913 1
 
< 0.1%
0.9865 1
 
< 0.1%
0.9848 1
 
< 0.1%
0.9814 1
 
< 0.1%
0.981 1
 
< 0.1%
0.9808 1
 
< 0.1%
0.9797 1
 
< 0.1%
0.9782 1
 
< 0.1%
0.9778 2
 
< 0.1%
Distinct4928
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
2024-09-24T15:54:17.244147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length91
Median length66
Mean length29.23
Min length5

Characters and Unicode

Total characters146150
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4874 ?
Unique (%)97.5%

Sample

1st rowAlabama A & M University
2nd rowUniversity of Alabama at Birmingham
3rd rowAmridge University
4th rowUniversity of Alabama in Huntsville
5th rowAlabama State University
ValueCountFrequency (%)
college 1902
 
10.2%
of 1231
 
6.6%
university 1179
 
6.3%
community 507
 
2.7%
school 376
 
2.0%
state 344
 
1.9%
technical 295
 
1.6%
institute 294
 
1.6%
beauty 276
 
1.5%
the 217
 
1.2%
Other values (3965) 11947
64.3%
2024-09-24T15:54:17.560495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 15191
 
10.4%
13574
 
9.3%
o 10470
 
7.2%
i 9810
 
6.7%
l 9458
 
6.5%
n 9045
 
6.2%
t 8945
 
6.1%
a 8674
 
5.9%
r 6748
 
4.6%
s 5800
 
4.0%
Other values (55) 48435
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 146150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 15191
 
10.4%
13574
 
9.3%
o 10470
 
7.2%
i 9810
 
6.7%
l 9458
 
6.5%
n 9045
 
6.2%
t 8945
 
6.1%
a 8674
 
5.9%
r 6748
 
4.6%
s 5800
 
4.0%
Other values (55) 48435
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 146150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 15191
 
10.4%
13574
 
9.3%
o 10470
 
7.2%
i 9810
 
6.7%
l 9458
 
6.5%
n 9045
 
6.2%
t 8945
 
6.1%
a 8674
 
5.9%
r 6748
 
4.6%
s 5800
 
4.0%
Other values (55) 48435
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 146150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 15191
 
10.4%
13574
 
9.3%
o 10470
 
7.2%
i 9810
 
6.7%
l 9458
 
6.5%
n 9045
 
6.2%
t 8945
 
6.1%
a 8674
 
5.9%
r 6748
 
4.6%
s 5800
 
4.0%
Other values (55) 48435
33.1%

City
Text

Distinct2113
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
2024-09-24T15:54:17.844528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length8.7846
Min length3

Characters and Unicode

Total characters43923
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1309 ?
Unique (%)26.2%

Sample

1st rowNormal
2nd rowBirmingham
3rd rowMontgomery
4th rowHuntsville
5th rowMontgomery
ValueCountFrequency (%)
city 121
 
1.9%
new 117
 
1.8%
san 116
 
1.8%
york 72
 
1.1%
chicago 58
 
0.9%
saint 57
 
0.9%
houston 47
 
0.7%
fort 46
 
0.7%
park 43
 
0.7%
los 42
 
0.7%
Other values (2005) 5630
88.7%
2024-09-24T15:54:18.279700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3973
 
9.0%
e 3917
 
8.9%
o 3388
 
7.7%
n 3371
 
7.7%
l 2844
 
6.5%
i 2762
 
6.3%
r 2638
 
6.0%
t 2306
 
5.3%
s 1957
 
4.5%
1354
 
3.1%
Other values (46) 15413
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3973
 
9.0%
e 3917
 
8.9%
o 3388
 
7.7%
n 3371
 
7.7%
l 2844
 
6.5%
i 2762
 
6.3%
r 2638
 
6.0%
t 2306
 
5.3%
s 1957
 
4.5%
1354
 
3.1%
Other values (46) 15413
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3973
 
9.0%
e 3917
 
8.9%
o 3388
 
7.7%
n 3371
 
7.7%
l 2844
 
6.5%
i 2762
 
6.3%
r 2638
 
6.0%
t 2306
 
5.3%
s 1957
 
4.5%
1354
 
3.1%
Other values (46) 15413
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3973
 
9.0%
e 3917
 
8.9%
o 3388
 
7.7%
n 3371
 
7.7%
l 2844
 
6.5%
i 2762
 
6.3%
r 2638
 
6.0%
t 2306
 
5.3%
s 1957
 
4.5%
1354
 
3.1%
Other values (46) 15413
35.1%

State
Text

Distinct59
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
2024-09-24T15:54:18.517100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10000
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st rowAL
2nd rowAL
3rd rowAL
4th rowAL
5th rowAL
ValueCountFrequency (%)
ca 489
 
9.8%
ny 349
 
7.0%
tx 295
 
5.9%
pa 288
 
5.8%
oh 249
 
5.0%
fl 238
 
4.8%
il 207
 
4.1%
nc 147
 
2.9%
ma 138
 
2.8%
mi 134
 
2.7%
Other values (49) 2466
49.3%
2024-09-24T15:54:18.798325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1610
16.1%
N 1043
10.4%
C 864
 
8.6%
M 684
 
6.8%
I 630
 
6.3%
L 601
 
6.0%
O 591
 
5.9%
T 560
 
5.6%
Y 429
 
4.3%
P 402
 
4.0%
Other values (14) 2586
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1610
16.1%
N 1043
10.4%
C 864
 
8.6%
M 684
 
6.8%
I 630
 
6.3%
L 601
 
6.0%
O 591
 
5.9%
T 560
 
5.6%
Y 429
 
4.3%
P 402
 
4.0%
Other values (14) 2586
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1610
16.1%
N 1043
10.4%
C 864
 
8.6%
M 684
 
6.8%
I 630
 
6.3%
L 601
 
6.0%
O 591
 
5.9%
T 560
 
5.6%
Y 429
 
4.3%
P 402
 
4.0%
Other values (14) 2586
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1610
16.1%
N 1043
10.4%
C 864
 
8.6%
M 684
 
6.8%
I 630
 
6.3%
L 601
 
6.0%
O 591
 
5.9%
T 560
 
5.6%
Y 429
 
4.3%
P 402
 
4.0%
Other values (14) 2586
25.9%

id
Real number (ℝ)

UNIQUE 

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243712.17
Minimum100654
Maximum461139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-09-24T15:54:18.927482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum100654
5-th percentile112384.1
Q1159022.5
median204197
Q3365552
95-th percentile455071.45
Maximum461139
Range360485
Interquartile range (IQR)206529.5

Descriptive statistics

Standard deviation115561.96
Coefficient of variation (CV)0.47417395
Kurtosis-0.77479718
Mean243712.17
Median Absolute Deviation (MAD)47461
Skewness0.85534624
Sum1.2185608 × 109
Variance1.3354567 × 1010
MonotonicityStrictly increasing
2024-09-24T15:54:19.048328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100654 1
 
< 0.1%
232450 1
 
< 0.1%
232681 1
 
< 0.1%
232672 1
 
< 0.1%
232618 1
 
< 0.1%
232609 1
 
< 0.1%
232575 1
 
< 0.1%
232566 1
 
< 0.1%
232557 1
 
< 0.1%
232423 1
 
< 0.1%
Other values (4990) 4990
99.8%
ValueCountFrequency (%)
100654 1
< 0.1%
100663 1
< 0.1%
100690 1
< 0.1%
100706 1
< 0.1%
100724 1
< 0.1%
100751 1
< 0.1%
100760 1
< 0.1%
100812 1
< 0.1%
100830 1
< 0.1%
100858 1
< 0.1%
ValueCountFrequency (%)
461139 1
< 0.1%
461120 1
< 0.1%
461111 1
< 0.1%
461087 1
< 0.1%
461032 1
< 0.1%
461023 1
< 0.1%
461014 1
< 0.1%
460996 1
< 0.1%
460987 1
< 0.1%
460978 1
< 0.1%

Interactions

2024-09-24T15:54:13.887288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:07.920820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:08.698775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:09.531312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:10.238501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:10.930915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:11.702024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:12.575389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:13.219207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:13.957721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:08.029196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:08.771989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:09.610280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:10.311714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:11.002006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:11.786690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:12.640686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:13.288070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:14.035574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:08.162939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:08.847335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:09.699254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:10.387454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:11.078387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:11.867222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:12.711679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:13.365169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:14.114347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:08.249436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:08.924244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:09.781629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:10.465102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:11.156246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:12.041744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:12.785336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:13.440673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:14.193765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:08.330169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:08.998408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:09.861629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:10.541643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:11.229410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:12.150375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:12.859799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:13.516801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:14.270254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:08.407395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:09.076174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:09.936765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:10.621201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:11.305876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:12.244922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:12.931649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:13.586402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:14.350949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:08.482954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:09.156091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:10.013621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:10.706978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:11.385489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:12.329397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:13.006359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:13.667312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:14.424495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:08.557524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:09.381373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:10.087695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:10.780887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:11.454831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:12.411541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:13.074734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:13.741624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:14.618740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:08.627503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:09.450218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:10.161897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:10.853694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:11.523688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:12.491274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:13.142545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-24T15:54:13.813131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-24T15:54:19.332395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Admission_RateAverage_SAT_ScoreCompletion_RateIn-State_TuitionMedian_Earnings_10_Years_AfterOut-of-State_TuitionPell_Grant_RateStudent_Sizeid
Admission_Rate1.000-0.386-0.255-0.238-0.176-0.2310.203-0.0450.067
Average_SAT_Score-0.3861.0000.8310.5480.6680.676-0.7180.277-0.023
Completion_Rate-0.2550.8311.000-0.0660.062-0.062-0.013-0.2850.179
In-State_Tuition-0.2380.548-0.0661.0000.6020.967-0.2920.507-0.262
Median_Earnings_10_Years_After-0.1760.6680.0620.6021.0000.654-0.4570.596-0.239
Out-of-State_Tuition-0.2310.676-0.0620.9670.6541.000-0.3640.603-0.310
Pell_Grant_Rate0.203-0.718-0.013-0.292-0.457-0.3641.000-0.4110.275
Student_Size-0.0450.277-0.2850.5070.5960.603-0.4111.000-0.329
id0.067-0.0230.179-0.262-0.239-0.3100.275-0.3291.000

Missing values

2024-09-24T15:54:14.753760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-24T15:54:14.985945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-24T15:54:15.120915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Admission_RateIn-State_TuitionOut-of-State_TuitionStudent_SizeAverage_SAT_ScoreMedian_Earnings_10_Years_AfterCompletion_RatePell_Grant_RateCollege_NameCityStateid
00.684010024186345196920.0406280.27380.6536Alabama A & M UniversityNormalAL100654
10.8668883221216127761291.0545010.63530.3308University of Alabama at BirminghamBirminghamAL100663
2NaN00228NaN37621NaN0.7769Amridge UniversityMontgomeryAL100690
30.7810118782477069851259.0617670.61920.2173University of Alabama in HuntsvilleHuntsvilleAL100706
40.966011068193963296963.0345020.28060.6976Alabama State UniversityMontgomeryAL100724
50.80061194032300313601304.0592210.72500.1788The University of AlabamaTuscaloosaAL100751
6NaN49908740963NaN335060.21770.2985Central Alabama Community CollegeAlexander CityAL100760
7NaN002465NaN50273NaN0.4127Athens State UniversityAthensAL100812
80.922391001934833071051.0443910.35770.4589Auburn University at MontgomeryMontgomeryAL100830
90.43741217632960252341292.0653370.80820.1254Auburn UniversityAuburnAL100858
Admission_RateIn-State_TuitionOut-of-State_TuitionStudent_SizeAverage_SAT_ScoreMedian_Earnings_10_Years_AfterCompletion_RatePell_Grant_RateCollege_NameCityStateid
4990NaN00298NaN268850.59930.6595Ogle School Hair Skin Nails-North DallasDallasTX460978
4991NaN00110NaN00.80430.2827The Salon Professional AcademySherwoodAR460987
4992NaN00200NaN349000.72290.2990The Salon Professional Academy-St CharlesSt. CharlesMO460996
4993NaN1581315813867NaN388300.28400.6188Mildred Elley-New York CampusNew YorkNY461014
4994NaN79957995434NaN422690.32140.4249National Paralegal CollegePhoenixAZ461023
4995NaN62766276112NaN24581NaN0.5303Carolina College of Biblical StudiesFayettevilleNC461032
4996NaN001315NaN344570.78260.1329Northeast Technology CenterPryorOK461087
4997NaN0031NaN00.58730.5254Allstate Hairstyling & Barber CollegeClevelandOH461111
4998NaN000NaN0NaNNaNOmega Graduate SchoolDaytonTN461120
4999NaN000NaN0NaNNaNJung Tao School of Classical Chinese MedicineSugar GroveNC461139